Overview

  • Intro/Context
    • Forests are globally important
    • Anthropocence effects
    • Global forest loss and gain and change
      • Global greening = India(Agriculture) + China(Forests)
  • Economics*Ecology = Landscape Extended Models
  • Network Analysis of China’s Greening
    • Global Scale
    • Local Scale
      • Landscape = Chen 2019
      • Resilience Analysis of China’s Forest LE-MRIO
  • Conclusions and Future Work
  • Acknowledgements

Forests are Important Globally

  • biodiversity
  • water and nutrient cycling
  • carbon storage
  • resources(wood, food)
  • culturally

The Anthropocene

  • Humans = dominant global impact -> Anthropocene
  • Global = Climate Change
  • Indirect Effects Significant

In the Anthropocene, Economy is Global Ecology

  • Economic trade data is a window into human impacts
  • Brief history of IO and ENA analyses
  • Global Trade Models
  • Trade Networks MRIO = Sectors + Regions
  • Environmental Extensions
  • Forested Landscapes and Embodied Trade Networks

Interactions/Trade = Complex Systems

  • Indirect effecs and The far reach of the city
  • Complex systems = many players and indirect effects matter (surprising)

How do we study forests in this context?

Background: Networks are Everywhere

Background: Networks are Everywhere

Background: Networks are Everywhere

Background: Ecological Network Analysis

  • Ecological network theory provides predictions and metrics (Lau 2017)
  • Systems theory provides strategies for inteventions
  • ENA <- Odums, MacArthur, Ulanowicz, Patten,
  • SNA -> ecological networks (Watts and Strogatz, etc.)
  • Structure linked to function (Donella Meadows)

Research: Why Chinese Forests?

  • Work = Forest Land Embodied in Trade

Global forest loss and gain and change

Global greening

Global greening = India(Agriculture) + China(Forests)

  • India is greening agriculturally

Global greening = India(Agriculture) + China(Forests)

  • China is greening through reforestation

A Brief History of Forest Time in China

  • China is big and diverse (Tropical to Alpine/Boreal)
  • Long history of human habitation in China
  • Historically, two primary regions of forestry
  • Forest conservation impacts harvest
  • Flows within China and among countries globally important

Research: Why Chinese Forests?

Research: Why Chinese Forests?

Network Analysis of China’s Greening

  • Global Scale
  • Local Scale
    • Landscape = Chen 2019
    • Resilience Analysis of China’s Forest LE-MRIO

A little money moves a lot of forest.

  • Tian et al. 2019 showed that China consumes an equivalent amount of domestic cropland as forest land, on the order of 10\(^6\) km\(^2\).
  • Looking at the domestic landuse productivity data for China, forests have the lowest monetary productivity.
  • Thus, per unit monetary output a relatively larger amount of forest land is used.

A little money moves a lot of forest.

  • Tian et al. 2019 showed that China consumes an equivalent amount of domestic cropland as forest land, on the order of 10\(^6\) km\(^2\).
  • Looking at the domestic landuse productivity data for China, forests have the lowest monetary productivity.
  • Thus, per unit monetary output a relatively larger amount of forest land is used.

Global Landuse Trade and China

Global Landuse Trade and China

Background: Input-Output Models

Background: Input-Output Models

  • How do we quantify and manage systems?
  • Input-Output Analysis provides a modeling framework
  • Direct consumption
  • Trade occurs among sectors == Indirect consumption
  • IO and MRIO models
  • A new equation for a new era in science E = F(I-A)^-1

Background: Input-Output Models

Background: Environmental Extension

  • Allows for indirect/consumption based accounting

Background: Environmental Extension

wos_mrio_time.jpg

wos_mrio_auth.jpg

wos_mrio_funding.jp

wos_mrio_field.jp

wos_mrio_region.jpg

Which metric?

  • Information

Why information metrics?

  • Related to Shannon Information/Diversity index
  • \[H = -\sum_i^n p_i log(p_i)\]

Methods: Model MRIO\(_{China}\)

Methods: Model MRIO\(_{China}\)

Methods: Environmentally Extended Model MRIO\(_{China}\)

Methods: Model Source

NEED TO ADD FIGURE WITH DATA FLOWS

Maybe check the Mi 2018 supp mat

Main Focus of Research

  1. What research has been done on forest or forest landscape embodied networks?
  2. What is the network structure? How can we characterize it?
  3. What can we say about the potential system dynamics based on network structure?

Research: Network Analysis

  • LEMRIO global (Tian 2019)
  • LEMRIO local (Chen 2019)
  • Your LE-MRIO China
  • Your ENA analysis
    • Small world
    • Modularity
    • Centrality
    • Control
    • Resilience

Research: Structural Analysis

  • Analysis = Structure = Robustness

Research: Structural Analysis

  • Overly efficient = Brittle
  • Overly redundant = Stagnant
  • Both can lead to niche opennings
  • Niches can then be filled by natural selection, adaptation or invasion

Forest Landscape Networks are More Efficient but Less Robust

Caveats

  • Limitations of MRIO
  • Potential impacts of storage lags and buffers

Future: Next up, climate change variability

  • Next up = Climate change impacts and global scale

Future: Next up, climate change variability

Future Work: Remote Sensing Trade Models

Q & A